English, Article, Journal or magazine article edition: BAYESIAN METHODS FOR COMPLETING DATA IN SPACE-TIME PANEL MODELS Carlos Llano; Wolfgang Polasek; Richard Sellner

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Bookmark: http://trove.nla.gov.au/version/1275
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • BAYESIAN METHODS FOR COMPLETING DATA IN SPACE-TIME PANEL MODELS
Author
  • Carlos Llano
  • Wolfgang Polasek
  • Richard Sellner
Physical Description
  • preprint
Notes
  • Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the rst to develop a unied framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the `indicators'). This paper develops a spatial Chow-Lin procedure for cross-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is dened by either km distance, travel time, contiguity and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.
  • Interpolation, Spatial panel econometrics, MCMC, Spatial
  • RePEc:rim:rimwps:05-09
Language
  • English
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